

import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment



class        A__(unittest.TestCase							):
						"""simple docstring"""






						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									a_						:						Optional[int] 							= inspect.getfile(accelerate.test_utils    )
									a_						:						Any 							= os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ["""scripts""", """test_script.py"""]    )
									a_						:						int 							= os.path.sep.join(
									    mod_file.split(os.path.sep    )[:-1] + ["""scripts""", """test_distributed_data_loop.py"""]    )
									a_						:						List[Any] 							= os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ["""scripts""", """test_ops.py"""]    )





						@require_multi_gpu
						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									print(F'''Found {torch.cuda.device_count()} devices.'''    )
									a_						:						Optional[Any] 							= ["""torchrun""", F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
									with patch_environment(omp_num_threads=1    ):
												execute_subprocess_async(lowerCAmelCase__  ,      env=os.environ.copy()    )





						@require_multi_gpu
						def 			UpperCamelCase__					(    self    )     -> Tuple:
									print(F'''Found {torch.cuda.device_count()} devices.'''    )
									a_						:						str 							= ["""torchrun""", F'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
									print(F'''Command: {cmd}'''    )
									with patch_environment(omp_num_threads=1    ):
												execute_subprocess_async(lowerCAmelCase__  ,      env=os.environ.copy()    )





						@require_multi_gpu
						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Optional[Any] 							= ["""torchrun""", F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__    )]
									with patch_environment(omp_num_threads=1    ):
												execute_subprocess_async(lowerCAmelCase__  ,      env=os.environ.copy()    )





						@require_multi_gpu
						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only'''    )
									a_						:						Any 							= ["""torchrun""", F'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
									with patch_environment(omp_num_threads=1  ,      cuda_visible_devices="""0,1"""    ):
												execute_subprocess_async(lowerCAmelCase__  ,      env=os.environ.copy()    )


if __name__ == "__main__":
	__snake_case				:      Tuple   		=			Accelerator()
	__snake_case				:      List[str]   		=			(accelerator.state.process_index + 2, 10)
	__snake_case				:      Union[str, Any]   		=			torch.randint(0, 10, shape).to(accelerator.device)

	__snake_case				:      Dict   		=			""""""

	__snake_case				:      Optional[Any]   		=			accelerator.pad_across_processes(tensor)
	if tensora.shape[0] != accelerator.state.num_processes + 1:
		error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
	if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
		error_msg += "Tensors have different values."
	if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
		error_msg += "Padding was not done with the right value (0)."

	__snake_case				:      Optional[Any]   		=			accelerator.pad_across_processes(tensor, pad_first=True)
	if tensora.shape[0] != accelerator.state.num_processes + 1:
		error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
	__snake_case				:      Dict   		=			accelerator.state.num_processes - accelerator.state.process_index - 1
	if not torch.equal(tensora[index:], tensor):
		error_msg += "Tensors have different values."
	if not torch.all(tensora[:index] == 0):
		error_msg += "Padding was not done with the right value (0)."

	# Raise error at the end to make sure we don't stop at the first failure.
	if len(error_msg) > 0:
		raise ValueError(error_msg)



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					UNetaDModel
			__a    :					ScoreSdeVeScheduler





			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Union[str, Any]:

							'''simple docstring'''


							super().__init__()
							self.register_modules(unet=lowerCAmelCase__ ,  scheduler=lowerCAmelCase__		)





			@torch.no_grad()
			def __call__(  self ,  lowerCAmelCase__ = 1 ,  lowerCAmelCase__ = 20_00 ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = "pil" ,  lowerCAmelCase__ = True ,  **lowerCAmelCase__ ,  )	->   Union[ImagePipelineOutput, Tuple]:

							'''simple docstring'''


							__lowercase					     =				self.unet.config.sample_size
							__lowercase					     =				(batch_size, 3, img_size, img_size)

							__lowercase					     =				self.unet

							__lowercase					     =				randn_tensor(lowerCAmelCase__ ,  generator=lowerCAmelCase__		) * self.scheduler.init_noise_sigma
							__lowercase					     =				sample.to(self.device		)

							self.scheduler.set_timesteps(lowerCAmelCase__		)
							self.scheduler.set_sigmas(lowerCAmelCase__		)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
											__lowercase					     =				self.scheduler.sigmas[i] * torch.ones(shape[0] ,  device=self.device		)

											# correction step
											for _ in range(self.scheduler.config.correct_steps		):
															__lowercase					     =				self.unet(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
															__lowercase					     =				self.scheduler.step_correct(lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		).prev_sample

											# prediction step
											__lowercase					     =				model(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
											__lowercase					     =				self.scheduler.step_pred(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		)

											__lowercase     ,	__lowercase					     =				output.prev_sample, output.prev_sample_mean

							__lowercase					     =				sample_mean.clamp(0 ,  1		)
							__lowercase					     =				sample.cpu().permute(0 ,  2 ,  3 ,  1		).numpy()
							if output_type == "pil":
											__lowercase					     =				self.numpy_to_pil(lowerCAmelCase__		)

							if not return_dict:
											return (sample,)

							return ImagePipelineOutput(images=lowerCAmelCase__		)